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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 storm. Although I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

How The Model Functions

The AI system works by identifying trends that traditional time-intensive scientific prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Still, the fact that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin said that while the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can make the DeepMind output more useful for experts by offering additional internal information they can use to evaluate the reasons it is coming up with its conclusions.

“The one thing that nags at me is that although these forecasts seem to be highly accurate, the results of the model is essentially a opaque process,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike most systems which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.